one noise variable, linear regression
## [1] "*************************************************************"
## [1] "one noise variable, linear regression"
## [1] "bSigmaBest 40"
## [1] "naive effects model"
## [1] "one noise variable, linear regression naive effects model fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2322 -0.6020 0.0120 0.5804 3.2574
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001467 0.019623 0.075 0.94
## n1 1.000321 0.038697 25.850 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8776 on 1998 degrees of freedom
## Multiple R-squared: 0.2506, Adjusted R-squared: 0.2503
## F-statistic: 668.2 on 1 and 1998 DF, p-value: < 2.2e-16
##
## [1] " train rmse 0.87711349635425"
## [1] " application rmse 1.15239485807949"
## [1] "one noise variable, linear regression naive effects model train rmse 0.87711349635425"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.140]
## [1] "one noise variable, linear regression naive effects model test rmse 1.15239485807949"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.293]
## [1] "effects model, sigma= 40"
## [1] "one noise variable, linear regression effects model, sigma= 40 fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4261 -0.6782 0.0002 0.6635 3.8954
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.141e-03 2.282e-02 0.050 0.960
## n1 -8.033e-05 6.554e-04 -0.123 0.902
##
## Residual standard error: 1.014 on 1998 degrees of freedom
## Multiple R-squared: 7.52e-06, Adjusted R-squared: -0.000493
## F-statistic: 0.01502 on 1 and 1998 DF, p-value: 0.9025
##
## [1] " train rmse 1.01322428589069"
## [1] " application rmse 0.995619017286281"
## [1] "one noise variable, linear regression Laplace noised 40 train rmse 1.01322428589069"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.446]
## [1] "one noise variable, linear regression Laplace noised 40 test rmse 0.995619017286281"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.599]
## [1] "effects model, jacknifed"
## [1] "one noise variable, linear regression effects model, jackknifed fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4251 -0.6776 -0.0009 0.6645 3.8913
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001465 0.022668 0.065 0.948
## n1 0.004279 0.038189 0.112 0.911
##
## Residual standard error: 1.014 on 1998 degrees of freedom
## Multiple R-squared: 6.285e-06, Adjusted R-squared: -0.0004942
## F-statistic: 0.01256 on 1 and 1998 DF, p-value: 0.9108
##
## [1] " train rmse 1.01322491166252"
## [1] " application rmse 0.99567998170435"
## [1] "one noise variable, linear regression jackknifed train rmse 1.01322491166252"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.752]
## [1] "one noise variable, linear regression jackknifed test rmse 0.99567998170435"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.905]
## [1] "********"
## [1] "one noise variable, linear regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9805 0.9963 1.0010 1.0010 1.0060 1.0240
## [1] 0.006949956
## [1] "********"
## [1] "********"
## [1] "one noise variable, linear regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.110 1.142 1.152 1.151 1.160 1.196
## [1] 0.01459732
## [1] "********"
## [1] "********"
## [1] "one noise variable, linear regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9803 0.9960 1.0010 1.0010 1.0060 1.0220
## [1] 0.006989054
## [1] "********"
## [1] "*************************************************************"
one variable, linear regression
## [1] "*************************************************************"
## [1] "one variable, linear regression"
## [1] "bSigmaBest 6"
## [1] "naive effects model"
## [1] "one variable, linear regression naive effects model fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3721 -0.6891 -0.0037 0.6848 3.7826
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20623 0.02260 9.125 <2e-16 ***
## x1 1.00000 0.03685 27.137 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.011 on 1998 degrees of freedom
## Multiple R-squared: 0.2693, Adjusted R-squared: 0.269
## F-statistic: 736.4 on 1 and 1998 DF, p-value: < 2.2e-16
##
## [1] " train rmse 1.01025938596012"
## [1] " application rmse 0.999915402747535"
## [1] "one variable, linear regression naive effects model train rmse 1.01025938596012"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1348]
## [1] "one variable, linear regression naive effects model test rmse 0.999915402747535"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1501]
## [1] "effects model, sigma= 6"
## [1] "one variable, linear regression effects model, sigma= 6 fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3911 -0.6877 -0.0039 0.6858 3.7951
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20587 0.02262 9.102 <2e-16 ***
## x1 1.00844 0.03726 27.064 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.011 on 1998 degrees of freedom
## Multiple R-squared: 0.2683, Adjusted R-squared: 0.2679
## F-statistic: 732.5 on 1 and 1998 DF, p-value: < 2.2e-16
##
## [1] " train rmse 1.0109902603753"
## [1] " application rmse 1.00200195619019"
## [1] "one variable, linear regression Laplace noised 6 train rmse 1.0109902603753"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1654]
## [1] "one variable, linear regression Laplace noised 6 test rmse 1.00200195619019"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1807]
## [1] "effects model, jacknifed"
## [1] "one variable, linear regression effects model, jackknifed fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3933 -0.6946 -0.0039 0.6875 3.7985
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2062 0.0227 9.084 <2e-16 ***
## x1 0.9871 0.0370 26.682 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.015 on 1998 degrees of freedom
## Multiple R-squared: 0.2627, Adjusted R-squared: 0.2623
## F-statistic: 712 on 1 and 1998 DF, p-value: < 2.2e-16
##
## [1] " train rmse 1.01481235978284"
## [1] " application rmse 1.00008428967326"
## [1] "one variable, linear regression jackknifed train rmse 1.01481235978284"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1960]
## [1] "one variable, linear regression jackknifed test rmse 1.00008428967326"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2113]
## [1] "********"
## [1] "one variable, linear regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9759 0.9976 1.0020 1.0020 1.0070 1.0180
## [1] 0.007127404
## [1] "********"
## [1] "********"
## [1] "one variable, linear regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9760 0.9978 1.0020 1.0020 1.0080 1.0180
## [1] 0.007123714
## [1] "********"
## [1] "********"
## [1] "one variable, linear regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9758 0.9982 1.0020 1.0030 1.0080 1.0200
## [1] 0.007149554
## [1] "********"
## [1] "*************************************************************"
one variable plus noise variable, linear regression
## [1] "*************************************************************"
## [1] "one variable plus noise variable, linear regression"
## [1] "bSigmaBest 15"
## [1] "naive effects model"
## [1] "one variable plus noise variable, linear regression naive effects model fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9216 -0.6181 0.0055 0.6225 3.5298
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20622 0.02058 10.02 <2e-16 ***
## x1 0.83459 0.03452 24.17 <2e-16 ***
## n1 0.78131 0.03844 20.33 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9203 on 1997 degrees of freedom
## Multiple R-squared: 0.3946, Adjusted R-squared: 0.394
## F-statistic: 650.8 on 2 and 1997 DF, p-value: < 2.2e-16
##
## [1] " train rmse 0.919591353886876"
## [1] " application rmse 1.12246743812363"
## [1] "one variable plus noise variable, linear regression naive effects model train rmse 0.919591353886876"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2556]
## [1] "one variable plus noise variable, linear regression naive effects model test rmse 1.12246743812363"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2709]
## [1] "effects model, sigma= 15"
## [1] "one variable plus noise variable, linear regression effects model, sigma= 15 fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4258 -0.6764 -0.0056 0.6806 3.7060
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.216252 0.022675 9.537 <2e-16 ***
## x1 1.000945 0.037153 26.941 <2e-16 ***
## n1 0.003584 0.001681 2.132 0.0331 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.012 on 1997 degrees of freedom
## Multiple R-squared: 0.2681, Adjusted R-squared: 0.2674
## F-statistic: 365.8 on 2 and 1997 DF, p-value: < 2.2e-16
##
## [1] " train rmse 1.01106882532701"
## [1] " application rmse 1.01243419816591"
## [1] "one variable plus noise variable, linear regression Laplace noised 15 train rmse 1.01106882532701"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2862]
## [1] "one variable plus noise variable, linear regression Laplace noised 15 test rmse 1.01243419816591"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3015]
## [1] "effects model, jacknifed"
## [1] "one variable plus noise variable, linear regression effects model, jackknifed fit model:"
##
## Call:
## lm(formula = formulaL, data = trainData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3986 -0.6920 -0.0077 0.6877 3.8126
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20643 0.02268 9.101 <2e-16 ***
## x1 0.98425 0.03698 26.614 <2e-16 ***
## n1 -0.07739 0.03479 -2.224 0.0262 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.014 on 1997 degrees of freedom
## Multiple R-squared: 0.2645, Adjusted R-squared: 0.2638
## F-statistic: 359.2 on 2 and 1997 DF, p-value: < 2.2e-16
##
## [1] " train rmse 1.01355772650768"
## [1] " application rmse 1.00913108707443"
## [1] "one variable plus noise variable, linear regression jackknifed train rmse 1.01355772650768"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3168]
## [1] "one variable plus noise variable, linear regression jackknifed test rmse 1.00913108707443"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3321]
## [1] "********"
## [1] "one variable plus noise variable, linear regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9845 0.9986 1.0020 1.0030 1.0080 1.0190
## [1] 0.006590893
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, linear regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.091 1.124 1.132 1.133 1.143 1.175
## [1] 0.01494531
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, linear regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9889 1.0020 1.0070 1.0070 1.0130 1.0270
## [1] 0.007442591
## [1] "********"
## [1] "*************************************************************"
one variable plus noise variable, diagonal regression
## [1] "*************************************************************"
## [1] "one variable plus noise variable, diagonal regression"
## [1] "bSigmaBest 17"
## [1] "naive effects model"
## [1] "one variable plus noise variable, diagonal regression naive effects model fit model:"
## x1 n1
## 1.000005 1.000333
## [1] " train rmse 0.958540237968956"
## [1] " application rmse 1.20618715828122"
## [1] "one variable plus noise variable, diagonal regression naive effects model train rmse 0.958540237968956"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3764]
## [1] "one variable plus noise variable, diagonal regression naive effects model test rmse 1.20618715828122"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3917]
## [1] "effects model, sigma= 17"
## [1] "one variable plus noise variable, diagonal regression effects model, sigma= 17 fit model:"
## x1 n1
## 0.991547116 0.004064617
## [1] " train rmse 1.03496916101217"
## [1] " application rmse 1.03804984767139"
## [1] "one variable plus noise variable, diagonal regression Laplace noised 17 train rmse 1.03496916101217"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4070]
## [1] "one variable plus noise variable, diagonal regression Laplace noised 17 test rmse 1.03804984767139"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4223]
## [1] "effects model, jacknifed"
## [1] "one variable plus noise variable, diagonal regression effects model, jackknifed fit model:"
## x1 n1
## 0.9871528 -0.1088369
## [1] " train rmse 1.03458802692346"
## [1] " application rmse 1.03176880530955"
## [1] "one variable plus noise variable, diagonal regression jackknifed train rmse 1.03458802692346"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4376]
## [1] "one variable plus noise variable, diagonal regression jackknifed test rmse 1.03176880530955"
## TableGrob (3 x 2) "arrange": 5 grobs
## z cells name grob
## 1 1 (2-2,1-1) arrange gtable[layout]
## 2 2 (2-2,2-2) arrange gtable[layout]
## 3 3 (3-3,1-1) arrange gtable[layout]
## 4 4 (3-3,2-2) arrange gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4529]
## [1] "********"
## [1] "one variable plus noise variable, diagonal regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9987 1.0160 1.0220 1.0210 1.0270 1.0420
## [1] 0.007821489
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, diagonal regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.170 1.208 1.218 1.218 1.228 1.262
## [1] 0.0164378
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, diagonal regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9978 1.0220 1.0280 1.0300 1.0350 1.1980
## [1] 0.01779162
## [1] "********"
## [1] "*************************************************************"